Nonlinear friction is a dominant factor of the complexity growth of feed drive dynamic behavior. In particular, a feed drive system equipped with rolling contact components such as ball screws and linear motion (LM) guides undergoes impalpable friction behavior. Therefore, proper modeling and compensation of friction in feed drive components play a crucial role in improving the precision of a feed drive system. In this study, to overcome the accuracy limitation of the simplified feed drive model, a distributed component friction model (DCFM) of a feed drive system is developed. Friction has been modeled as a single element of entire feed drive system in the conventional model. However, the proposed DCFM is composed of models of individual feed drive components such as ball screw and LM guide including their friction behaviors. To take advantage of the DCFM by considering both presliding and sliding friction behaviors of each component, LuGre model is applied. For experimental verification of the accuracy of the proposed DCFM, a specially designed feed drive testbed is constructed. Using the testbed, the friction forces of ball screws and LM guides are measured independently. Using the DCFM, a friction observer is built for real-time monitoring and compensation of the friction force. A proportional-derivative feed drive position controller with an observer-based friction compensator is implemented and the experimental results are presented.

Optimization of machining conditions is important in determining productivity and machining quality. This study proposes a method for optimizing the machining conditions of a machine tool using a digital twin of a commercial machine tool comprising physical models of a controller, feed drive systems, and cutting load. The digital twin is constructed and evaluated based on machining experiments, and a genetic algorithm is adopted to determine the machining conditions to minimize the machining time and production cost. The optimal feed rate and spindle speed are obtained for each line of the part program when the cutting force is limited. The machining results demonstrate the effectiveness of the proposed method. After optimization for maximizing the machining speed, the machining time decreased by 16.9%. Similarly, after optimization for minimizing the production cost, the production cost reduced by 36.4%.